Rail infrastructure owners are motivated to replace the time consuming and subjective process of manual crosstie (track) inspection with objective and automated processes. The motivation is driven by a desire to improve rail safety in a climate of increasing annual rail traffic volumes and increasing regulatory reporting requirements. Objective, repeatable, and accurate track inventory and condition assessment also provide owners with the innovative capability of implementing comprehensive asset management systems which include owner/region/environment specific track component deterioration models. Such rail specific asset management systems would yield significant economic benefits in the operation, maintenance and capital planning of rail networks.
A primary goal of such automated systems is the non-destructive high-speed assessment of railway track infrastructure. Track inspection and assessment systems currently exist including, for example, Georgetown Rail (GREX) Aurora 3D surface profile system and Ensco Rail 2D video automated track inspection systems. Such systems typically use coherent light emitting technology, such as laser radiation, to illuminate regions of the railway track bed during assessment operations.
An important consideration after field data collection of railway data is the manner in which the data is processed. One of the most time-consuming tasks is to identify different railway track features and to categorize and track such railway track features.
What is needed, therefore, is a robust and reliable system for analyzing and processing data collected during and/or after a high speed assessment of a railway track. What is also needed is a system that is able to quickly and accurately identify railway track features and associate measured parametric data with those features.